1. Introduction: Ushering in Precision Education with Personal
Genomics
Personal
genetics, often described as an “unstoppable force,” is rapidly becoming more
accessible to the public due to decreasing costs. Now, numerous companies offer
genetic testing or “genome scans” to individuals seeking information about
their ancestry, disease risks, behavioral tendencies, and even athletic
potential. While still in its infancy, the field of personal genomics holds
immense promise, evidenced by its various applications in healthcare (Kung
& Gelbart, 2012).
The
last five years have witnessed a surge in interest in integrating genetic
information into education. This integration holds the potential to personalize
learning experiences and optimize educational processes (Sabatello, 2018; Kovas
et al., 2016; Panofsky, 2015; Asbury, 2015; Thomas et al., 2015; Asbury &
Plomin, 2013). Notably, a 2018 study published in Nature Genetics by the SSGAC
and a consumer genetics company demonstrated that educational attainment has a
heritable component and is linked to various social, economic, and health
outcomes (Lee, 2018).
While
accounting for individual variations in learning aptitude is a crucial issue in
educational research, the role of genetic factors is often ignored. The field
primarily focuses on developing uniform approaches to suit all students, which
challenges educators who recognize that each student possesses a unique profile
of psychological, cognitive, and emotional characteristics. According to
genetic research, each learner has a distinct genetic makeup comprising a
unique DNA sequence, specific gene expression patterns, and a naturally
individualized way in which their genes interact with the environment (Kovas et
al., 2016).
Therefore,
adapting the educational process based on personal genetic data holds promise
for better meeting individual needs and improving academic achievement (Krasa
& Shunkwiler, 2009; Kovas et al., 2016). However, educational success
requires a multifaceted approach beyond single interventions or programs. Key
elements include student ability and motivation, teacher expertise,
instructional methods, and curriculum design (Kovas et al., 2016). While
correlations exist between genetic differences and human traits, both physical
and mental, including educational attainment, recent research has focused on
understanding the specific genetic markers linked to aspects such as
intellectual capacity, learning difficulties, and verbal skills. The growing
interest in using genetic research to develop educational processes and systems
should not overshadow the crucial role of the environment in shaping individual
growth and learning outcomes (Baron-Cohen et al., 2014; Docherty et al., 2010).
Recently,
Educational Genomics has emerged as a new research field that investigates how
detailed information about the human genome – specifically, DNA variables – can
be used to determine certain educational attributes, such as reaction time,
memory, academic success, and learning abilities. It is believed that
educational genomics will contribute in the future by enabling
educational institutions to create specially designed curriculum programs based
on the learner’s DNA profile.
Information about DNA variants can also be used to predict the
distinguished capabilities of the learner so that they can be best developed
within a tailored, rich environment in the classroom (Gaussian, 2016). Although research
findings emphasize that individual learning differences, genetics, and their
implications for education are rare. Recent years have witnessed an interest in
research that investigates personalized medicine, which investigates genetic
differences among people to predict potential health problems, thus enabling
physicians to modify treatment and prevention methods (Collins, 2010).
Similarly, the same information can be used to develop the field of educational
genomics and it holds immense potential for the advancement of the subject,
potentially paving the way for personalized learning approaches (Conley &
Fletcher, 2017; McCarthy & Mahajan, 2018; Plomin,
2018), so that every child can be allowed to reach his/her full potential
(Gaussian, 2016).
Martschenko et al. (2019) discussed developments in
genetics research and their potential implications for education. They
emphasized that genetics plays a significant role in understanding individual
growth and human behaviors. They also believed that the results of genetics
research would influence various human activities. They emphasized that the
role of the environment in education is no less important than the role of the
human genome, as individual skills can only be developed by providing the
appropriate environment. Practitioners of educational genomics and behavioral
genetics identified that the interaction between educational environments and
learners’ distinctive genetic makeup plays a crucial role in shaping learning
abilities, educational achievement, and motivational levels, leading to
substantial variations among individuals (Kovas et al., 2007).
Genetics
research can also contribute to deepening the understanding of learning
difficulties and development paths that can contribute to how educators can
deal with different learning difficulties of individuals by providing
information that contributes to identifying appropriate intervention strategies for each
learner individually. It can also contribute in the long term to forecasting
risks at the individual level (Daphne et al., 2019). Recent research suggests
that incorporating genetic insights into education could revolutionize
learning, allowing us to cater to individual needs and maximize potential.
Research by Asbury and Plomin (2013) proposed three key strategies for
educators and policymakers: acknowledging and embracing individual differences in
abilities, tailoring educational approaches to individual genetic profiles, and
mitigating the negative impacts of disadvantaged backgrounds through early
intervention. Advances in molecular genetics, such as
the use of microarrays for analyzing DNA markers, are illuminating the
biological processes that influence gene expression in response to various
environments (Plomin et al., 2012).
Genome-wide
association studies (GWAS) have revealed the biological basis of learning by
identifying genetic factors linked to language, reading, and math skills.
(Meaburn et al., 2008). Furthermore, GWAS have uncovered genetic variations
associated with educational attainment, suggesting a significant role of genes
in learning ability (Rietveld et al., 2013). These advancements in molecular
genetics hold promise for the early diagnosis of learning disabilities and the
identification of relevant genes. This, in turn, could pave the way for
personalized educational approaches tailored to individual cognitive and
motivational profiles (Kovas et al., 2016).
1.2 Gene-environment Interplay
The
traditional view of the impact of genetics and environment on child development
has recently undergone a significant shift. Instead of adhering to a simplistic
dichotomy of genes versus environment, the current perspective emphasizes the
intricate interplay between genes and the environment throughout the
developmental process. Contemporary research now directs its attention to
examining the co-action of molecular genetic mechanisms and the environment
along two pathways. The first path delves into how the environment influences
gene expression through epigenetic processes, while the second path explores
how genetics shape an individual’s perceptions and choices in their
environment, known as gene–environment correlations (Kovas & Malykh, 2016).
However, it is crucial to recognize that the majority of common individual
growth and human behaviors are polygenic. The development of specific learning
abilities or competencies, and the degree of mastery achieved, is determined by
the combination of numerous genes and the interaction between genes and the
environment.
Thus, this study aims to reveal how Educational Genomics will help in developing
precision education in the near future, and what the future prerequisites
should be considered for ensuring successful implementation by answering the
following questions:
1.
How will Educational Genomics help in
developing precision education experiences in the near future (next 5-10
years)?
2.
What are the key future requirements
that need to be prioritized for integrating genomics into precision education
experiences in the near future, especially from an educational standpoint?
3.
Are there statistically significant
differences in the responses of the participants based on their demographic
characteristics?
2. Literature Review
2.1 Precision Education: Definition and
Structural Components
The
prefix “precision” has become increasingly prevalent across distinct domains of
scientific and technological research, encompassing fields such as medicine,
agriculture, and education (Kuch et al., 2020). New forms of knowledge are
being generated through the application of “precision” technologies, which
involve the creation of new data and sophisticated measurement techniques to
precisely assess target cases and implement interventions (Williamson, 2019a).
Precision
education is both an interdisciplinary ‘science of learning’ and an educational model informed by the
sciences of the human mind, brain, and genome. Williamson
(2019a) defines precision education as a burgeoning fusion of psychological,
neuroscientific, and genetic proficiency that is underway, with a specific
focus on leveraging advanced computational technologies to generate “intimate
data” about students’ bodies and their biological correlations with the
learning process.
At
its core, precision education revolves around customizing preventative and
intervention approaches for individuals, guided by the most robust and current
evidence. It also utilizes innovative research designs to answer the
fundamental question: “What intervention worked with whom and how did it work?”
This is achieved through deep analysis of the root causes of specific
scholastic, emotional, social, and physical health problems. This analysis
informs the development of suitable, tailored, and personalized prevention and
intervention practices that match each child’s unique needs (Cook et al.,
2018).
The
future aspiration for precision education is to be firmly grounded in an
interdisciplinary science of learning that informs the implementation of
precise practices in education. This aims to ultimately overcome the
shortcomings of the “one size fits all” model, where one educator delivers the
same material to a group of learners without individualization. It is important
to note that precision education is still in its early stages of development,
and there is no single, universally agreed-upon definition. However, a common
theme amongst proposed definitions is the emphasis on providing adaptive,
immediate, precise, detailed, and contextualized personalized interventions
(Lian & Sangarun, 2017).
One
of the promises of precision education is the increased use of learner data to
inform and personalize educational practices (Williamson, 2019b). It shares a
core tenet with precision medicine: the desire to improve personalized
interventions based on individual assessments, predictions, treatments, and
prevention strategies for specific needs. Therefore, the primary goal of
genetic-based precision education is to enhance diagnosis, prediction,
treatment, and prevention strategies specifically tailored to at-risk students
through individualized approaches (Lu et al., 2018; Yang, 2019).
Precision
education has certain core characteristics (Mazoue, 2013):
1. Its
research-based approach and methodology aim to produce learning-optimized
course structures.
2. It
effectively individualizes learning.
3. It
centers on competencies as a basis for education.
4. It
exhibits scalability in its application.
5. It
proves to be cost-efficient.
The
initial facet of precision education entails a meticulous analysis of the
student’s challenges, delving into genuine needs that may involve deficiencies
in adaptive skills, such as recognizable fluency, acquisition, or performance
deficits. Environmental factors, such as the absence of instructional alignment
or adult attention, can also influence skill deficits. Theoretical paradigms
such as instructional hierarchy, skill-by-treatment interaction, or functional
assessment may be employed in this problem analysis. The second facet revolves
around flexible interventions tailored to align with individual student needs,
preferences, and motivation, effectively addressing identified problems. These
interventions encompass versatile multi-functional strategies personalized to
suit each student‘s unique requirements. Monitoring constitutes the third
pivotal element in precision education, with the objective of ongoing formative
evaluation of the student’s progress resulting from the tailored intervention.
The outcomes of monitoring serve as the basis for adjusting the intervention to
better align with evolving needs. Collaboration, the fourth component of precision
education, is considered essential for making data-driven, timely, and
judicious decisions. This collaboration plays a crucial role in determining
whether to modify, implement an alternative, or conclude the intervention based
on the available information (Cook et al., 2018).
The researcher defines Genomics-based
precision education as the design and development of educational services and
opportunities that are commensurate with the characteristics of the learners.
This approach enables educational practitioners to classify learners into
subgroups based on their genetic profiles. Subsequently, it enables the
adaptation of the educational process to personal genetic data, thereby meeting
the individual needs of students and enhancing their academic achievement.
Furthermore,
precision education focuses on developing and designing preventive or
supportive interventions that target those who will benefit from them. This
avoids exposing individuals who are unlikely to benefit from unnecessary
interventions, thus preventing wasted time and resources.
Genetic
researchers have definitively linked genes and educational achievement, marking
a significant advancement in the burgeoning field of educational genetics.
Although still in its early stages, educational genomics is prompting a growing
call to leverage genomic insights for crafting personalized educational
approaches. This methodology envisions tailoring educational interventions to
seamlessly harmonize with each child’s unique genetic makeup (Grigorenko,
2007). Preliminary indications suggest a rising acceptance of genomics in
education, reflecting the potential for genomic data in shaping effective
interventions from birth onwards, as conceptualized by Francis Collins
(Sabatello, 2018).
In
the near future, genetics research may enable precision education to use
individual genotypes to tailor personalized educational activities for each
child. This will be achieved by investigating learning mechanisms that explain
why some current teaching and learning methods work, in addition to identifying
which future methods might also be effective and for whom (Thomas, 2013).
Asbury
and Plomin (2013) propose that if educational systems optimize educational
environments, the remaining differences between learners may be due to genetic
makeup. Therefore, it is crucial to align educational environments with these
genetic variations to maximize the capabilities of learners. Recent genetic studies
confirm that genetic influences are not fixed; instead, they are dynamic. This
means that the same genes can exhibit different expressions in different
environments and at different stages of human development (Kovas et al., 2007;
Kovas et al., 2013). Genetics can enable education systems to focus on specific
genes among learners and develop precision education
activities that can contribute to maximizing learners’
potential and tailoring interventions that depend on
learners ‘genetic makeup’ (Söderqvist et al., 2013).
Behavioral
geneticist Robert Plomin and colleagues foresee “precision education” based on “genome-wide
polygenic scoring” (GPS) as a potentially innovative method of customizing
education. This is because polygenic scores can be used to predict a student’s
academic achievement directly from their DNA. As a result, the potential for
early intervention and individualized learning becomes attainable.
Precision
education requires genetic data likely related to the brain, as it is
responsible for perception and learning. Therefore, the focus will be on both
the brain’s structure and its cognitive functions. Quantitative genetic studies
highlight essential genes that precision education needs to investigate
thoroughly. These include genes that impact performance in fundamental school
disciplines, cognitive abilities, individual traits, mental health, physical
well-being, self-efficacy, behavioral challenges, overall welfare, and
perspectives on the educational environment (Krapohl et al., 2014).
2.4 The Difficulties Associated with Employing Genetic Information to Derive Tangible
Applications for Precision
Education
While some advocate for genomically
informed precision education, the promise of readily available genetic data for
personalized learning faces significant challenges and raises important
ethical, legal, and social issues, such as potential misuse in educational
settings (Sabatello, 2018).
Educational
researchers must actively investigate approaches to tackle these challenges,
anticipating the need for responsive measures. Before devising precision
education methodologies, a thorough examination of privacy and insurability
considerations regarding students’ personal genetic data is essential. This
transformative shift necessitates that educational practitioners adopt new
ethical and legal obligations in managing sensitive and potentially unfamiliar
confidential genetic information.
Policymakers
should closely examine the essential prerequisites for implementing
genomicization in precision education. After reviewing the current literature,
the researcher identified and categorized these prerequisites into four key
areas (Kuch et al., 2020; Williamson, 2019a; Lu et al., 2018; Sabatello, 2018;
Cesarini & Visscher, 2017):
1. Involve
students and pertinent stakeholders, ranging from parents and students to healthcare
professionals, educators, and policymakers.
2. Research
priorities and evidence generation.
3.
Application of effective precision
education practices.
4.
Datafication, Ownership, privacy, and
sharing of data
1.
Engage Students and Stakeholders: Putting
Students at the Core of Precision Education Priorities
Students
are the main pillar of genomics-based precision
education, as their genetic and personal information
is the essential component of precision education and the basic indicators
for identifying their needs. Students’ confidence that their data will be used
effectively and ethically is of great importance. Therefore, attention must be
given to having enhanced transparency in genetic and personal information
systems and building channels of communication with the relevant stakeholders.
Therefore, the following future prerequisites should be considered
(Sabatello, 2018):
A. Building
confidence in genomics-based precision among educational practitioners and
stakeholders.
B. Acceptance
of Genomics-based precision education by academics, students, and stakeholders.
C. Determining
which institutional changes come with Genomics-based precision education.
D. Explaining
the rationale and importance of adopting Genomics-based precision education to
the stakeholders.
2. Evidence Generation and Research
Priorities
A. Imminent
scientific investigations into the genetic aspects of educational achievement
and its precursors, such as intelligence, personality dimensions, dyslexia, and
attention challenges, combined with the discovery of genetic elements
influencing educational outcomes, may elucidate the intricate ways in which
genes contribute to shaping learning success. This newfound awareness could
potentially equip educators with the tools to tailor proactive interventions
informed by genetic data, addressing the unique needs of individual students
(Cesarini & Visscher, 2017).
B. It
is imperative to focus on cost-effectiveness research, aiming to identify the
most urgent learning disabilities within particular student demographics.
Following this, it becomes vital to precisely determine which intervention
strategies achieve their goals effectively when aligned with specific genetic
data. Educators should strive to understand the factors contributing to
improved outcomes and the implementation of more effective educational
methodologies (Williamson, 2019a).
3. Application of Effective
Precision Educational Practices
A. To
fully realize the potential of genomicization of precision education, educational
practitioners should first develop new precision education practices. Before
utilizing a student’s genetic information, it is crucial to undertake
innovative research to determine the effectiveness of interventions for
specific individuals, understand the mechanisms of their efficacy, and develop
robust preventive and intervention strategies tailored to the distinct
requirements of each learner. This approach should be intricately informed by
the dynamic interplay between educational environments and the unique genetic
profiles of learners (Williamson, 2019a).
B. Technology
information and other analytical tools, such as learning analytics and adaptive
learning software, should be employed to support optimal decision-making about
the appropriate interventions (Williamson, 2019a).
4.
Datafication, Ownership, Privacy, and Sharing of Data
A.
Precision education faces not only a
wealth of information but also the rapid integration of diverse practices
within its realm. Those involved in education need effortless access to
reliable information and guidelines in this dynamic landscape. In this regard,
learning analytics and educational big data can be applied as a robust
conceptual framework to promote the analysis and prediction of students’
performance, providing appropriate and timely interventions based on student
learning profiles (Williamson, 2019a; Lu et al., 2018). Education, among other
domains, has been transformed by datafication, which involves converting
various aspects of education into quantifiable digital data that can be analyzed
to inform policy-making (Kuch, Kearnes, & Gulson, 2020).
B. Thus,
the materialization of precision education relies on an advanced data analytics
platform. This system is anticipated to aid educators in formulating precise
learning strategies and appraising the progress of students through these
strategies.
C. To
optimally use students’ genetic information to develop suitable interventions,
the availability of comprehensive family histories will be a must. Despite
having access to the complete genetic data of a student, it remains essential
to decipher this information within the frameworks of both biological and
environmental contexts in which the genomes manifest.
D. Developing
an ethical framework that addresses potentially related risks and concerns of
providing personal genetic information and privacy protection mechanisms is
crucial.
E. Genomics-based-precision
education information should be provided for students via their electronic
portals connected to their electronic learning records. Cultural contexts should
be taken into account, meaning that the records and materials must be designed
at the level of different cultures (Sabatello, 2018).
F.
Develop guides that incorporate
different levels of initial use of Genomics-based precision, controls,
procedures, and conditions for continuous collection of data on the efficacy of
Genomics-based precision education (Sabatello, 2018). Establish standards for
shared data elements to record information in electronic records (Sabatello,
2018).
3. Methodology and Procedures
In
the quest to comprehend the fundamental elements necessary for the successful
integration of precision education based on genomics, this exploratory
descriptive research utilized a questionnaire as its principal instrument for
data collection. The aim was to recognize and prioritize the forthcoming
requirements vital for the triumphant implementation of this educational
paradigm. The questionnaire, meticulously devised by the researcher, took shape
after an exhaustive examination of relevant literature (Kuch et al., 2020;
Williamson, 2019a; Lu et al., 2018; Sabatello, 2018; Cesarini & Visscher,
2017).
The
questionnaire consisted of two sections: the first section included (22) items, distributed in four domains: The domain of
engaging students and the relevant stakeholders included (4 items), the domain of research priorities and evidence generation
included (6 items), the domain of application included (3 items), and the
domain of datafication, ownership, privacy and sharing of data embraced (9
items). The questionnaire utilized a Likert-type scale
with five points (1 - Very low importance, 2 - Low importance, 3 - Moderate
importance, 4 - High importance, and 5 - Very high importance) to assess the
relative importance of identified future prerequisites and rank them
accordingly. The second segment comprised statements related to pertinent
demographic information required from potential participants.
To
measure internal consistency, the questionnaire’s overall Cronbach’s alpha
coefficient (α) was calculated, indicating values ranging from 0.84 to
0.91 across items and domains. The overall stability, depicted by a coefficient
of 0.89 (as outlined in Table 1), is notably high, effectively meeting the
study’s objectives. Face validity was ensured through a thorough review by
seven academic experts, leading to modifications based on their feedback. A
pilot test with 11 experienced teaching staff further guided adjustments to
improve the questionnaire’s reliability.
Table
1: Cronbach’s alpha values for
the questionnaire and its respective domains
|
Total Stability
|
Engage students and the relevant
stakeholders
|
Evidence generation
|
Application
|
Ownership, privacy, and sharing of data
|
|
|
|
Stability Coefficient
|
Number of items
|
Stability Coefficient
|
Number of items
|
Stability Coefficient
|
Number of items
|
Stability Coefficient
|
Number of items
|
|
0.89
|
22
|
0.91
|
4
|
0.90
|
6
|
0.84
|
3
|
0.84
|
9
|
|
|
|
|
|
|
|
|
|
|
|
To ensure a rich and multifaceted
representation of the Egyptian College of Education faculty, this study
employed a rigorous, two-pronged selection approach. Firstly, geographical and
institutional diversity were considered. Participants hailed from five
geographically distinct universities, situated across urban and rural settings:
Ain Shams (Cairo), Tanta (Lower Egypt), Assiut (Upper Egypt), Zagazig (Delta),
and Helwan (Greater Cairo). Furthermore, colleges represented a spectrum of
sizes (large, medium, and small), reflecting diverse institutional contexts and
resources.
Secondly,
expertise and perspective diversity were crucial. The 169 participants were
carefully chosen not only for their expertise in specific fields (Education,
Science, and Literature) but also for their ability to offer varied academic
backgrounds and viewpoints. This deliberate selection strategy aimed to capture
the multifaceted experiences and opinions within Egyptian colleges of
education, ultimately strengthening the depth and breadth of the study’s
findings.
Before
participation, all individuals received information about the questionnaire’s
purpose. To minimize response bias, including social desirability bias, the
author implemented measures ensuring anonymity and confidentiality during the
data collection process.
Table 2: Profile of participants
|
Study variables
|
Variable levels
|
Frequency (f)
|
Percentage (%)
|
|
Gender
|
Male
|
79
|
46. 745
|
|
|
Female
|
90
|
53.254
|
|
|
Total
|
169
|
100
|
|
Academic
Rank
|
Professor
|
57
|
33. 728
|
|
|
Associate
Professor
|
67
|
39.645
|
|
|
lecturer
|
45
|
26.627
|
|
|
Total
|
169
|
100
|
|
Specialized
domain
|
Educational
|
63
|
37. 278
|
|
|
Scientific
|
49
|
28.994
|
|
|
Literary
|
57
|
33.728
|
|
|
Total
|
169
|
100
|
3.2 Ethical Considerations
This
study did not involve any intervention, sensitive personal data, or vulnerable
populations. Participation in the questionnaire was entirely voluntary, and all
respondents were informed of the research’s purpose. Anonymity and
confidentiality were strictly maintained, and no identifiable personal
information was collected. The data was used solely for academic research
purposes, in accordance with established ethical research practices.
3.3 Statistical Analysis
The processing of data involved using the
SPSS Analysis program, version 23, to conduct a descriptive analysis of the
gathered information. The mean score served as a metric to evaluate respondents’
levels of agreement regarding the future prerequisites for the effective
implementation of genomics-based precision education. The interpretation
criteria are outlined in Table 3:
M = 1.00-1.50:
Very low importance
M = 1.50-2.50: Low
importance
M = 2.50-3.50:
Moderate importance
M = 3.50-4.50:
High importance
M = 4.50-5.00:
Very high importance
The
statistical analysis also included the application of Cronbach’s alpha formula,
the Z-test, and the Pearson correlation coefficient.
4. Results and Discussion
The
questionnaire was divided into two main sections: The
first section included four domains of future prerequisites for the successful
implementation of genomics-based precision education: engaging students and
relevant stakeholders, evidence generation for precision education application,
and ownership, privacy, and sharing of data. The investigator employed closed-ended
Likert scale statements (ranging from “Very low importance” to “Very high
importance”) for participant responses. The second part was dedicated to
gathering demographic data from participants. The principal findings are
outlined as follows:
Table
3: Categorization of mean scores
into respective weight categories
Q2. What is the
relative importance of future prerequisites for the successful
implementation of Genomics-based precision education from the perspectives of academic staff at
five Egyptian colleges of education, according to the
variable of gender (males, females), academic rank (professor, associate
professor, and lecturer), and specialized domain (Educational, Scientific, and
Literary)?
To
answer this question, calculations were performed for means, relative importance,
and item rankings, as detailed in Table 4. The interpretation of results relied
on the means and relative importance values, as outlined in Table 3.
Table
4: Mean score of the
academic staff’s
points of view on the
future prerequisites according to the variable of
gender
|
Items
|
Female
|
Male
|
|
M
|
SD
|
RI
|
R
|
M
|
SD
|
RI
|
R
|
|
First domain: engage
students and the relevant stakeholders
|
|
1 -1
|
Building confidence in
Genomics-based precision among educational practitioners and stakeholders.
|
4.811
|
0.394
|
0.962
|
1
|
4.848
|
0.361
|
0.970
|
2
|
|
1 -2
|
Acceptance of
Genomics-based precision education by academics, students, and stakeholders.
|
4.778
|
0.418
|
0.956
|
2
|
4.939
|
0.361
|
0.988
|
1
|
|
1 -3
|
Determining which
institutional changes come with Genomics-based-precision education.
|
4.700
|
0.461
|
0.940
|
3
|
4.608
|
0.564
|
0.922
|
4
|
|
1 -4
|
Explaining the
rationale and importance of adopting Genomics-based precision education to
the stakeholders.
|
4.633
|
0.570
|
0.927
|
4
|
4.785
|
0.414
|
0.957
|
3
|
|
Second domain: research
priorities and evidence generation
|
|
2 -1
|
Identifying genes
influencing students’ achievement in core school subjects, intelligence,
self-efficacy, behavioral problems, well-being, and perceptions of the school
environment.
|
4.467
|
0.706
|
0.876
|
5
|
4.418
|
0.744
|
0.884
|
6
|
|
2 -2
|
Identifying which
competencies staff and students need for teaching and learning in
Genomics-based-precision education environments.
|
4.356
|
0.739
|
0.876
|
6
|
4.406
|
0.725
|
0.881
|
7
|
|
2 -3
|
Identifying which
prevention and intervention strategies will achieve their goals when they are
implemented based on certain genetic data.
|
4.356
|
0.739
|
0.871
|
7
|
4.532
|
0.637
|
0.906
|
5
|
|
2 -4
|
How to tailor
prevention and intervention practices to individual learners based on the
best available evidence.
|
4.322
|
0.747
|
0.864
|
8
|
4.392
|
0.775
|
0.878
|
8.5
|
|
2 -5
|
How to align
educational environments with genetic differences to maximize learners’
potential.
|
4.289
|
0.753
|
0.858
|
9
|
4.392
|
0.775
|
0.878
|
8.5
|
|
2 -6
|
Conducting
cost-effectiveness research to justify investments of personnel and
technological resources for applying Genomics-based precision education
(i.e., return on investment).
|
4.244
|
0.769
|
0.849
|
10
|
4.342
|
0.766
|
0.766
|
10
|
|
Third domain:
application
|
|
3 -1
|
Availability of
suitable prevention and intervention practices that match each learner’s
needs based on the interaction between educational environments and students’
distinctive genetic profiles.
|
4.144
|
0.801
|
0.829
|
11.5
|
4.266
|
0.858
|
0.853
|
11
|
|
3 -2
|
Developing a robust,
practical framework to guide the adaptation of educational processes in terms
of students’ genetic profiles.
|
4.144
|
0.801
|
0.829
|
11.5
|
4.253
|
0.869
|
0.851
|
12
|
|
3 -3
|
Developing educational
curricula to maximize children’s different genetic potentials.
|
4.089
|
0.857
|
0.818
|
13
|
4.228
|
0.847
|
0.846
|
13.5
|
|
Fourth domain:
datafication, ownership, privacy, and sharing of data
|
|
4 -1
|
Availability of every
student’s genetic makeup.
|
4.089
|
0.802
|
0.818
|
14
|
4.127
|
0.882
|
0.825
|
15
|
|
4 -2
|
Availability of
students’ comprehensive family histories to interpret the information in
terms of the biological and environmental contexts in which the students’
genomes are expressed.
|
4.078
|
0.851
|
0.816
|
15
|
4.126
|
0.882
|
0.825
|
16
|
|
4 -3
|
Developing a robust
conceptual framework to analyze and predict students’ performance in terms of
his/her distinctive genetic profile.
|
4.056
|
0.826
|
0.811
|
16
|
4.228
|
0.876
|
0.846
|
13.5
|
|
4 -4
|
Adopting datafication
to transform the different features of education into quantifiable digital
data that can be exploited through analytics to inform policy-making.
|
4.033
|
0.827
|
0.807
|
17
|
4.114
|
0.891
|
0.823
|
17
|
Note:
M Mean, SD Standard Deviation, RI Relative Importance, Ranking
Table
4 presents the mean scores, standard deviations, relative importance (RI), and
ranking (R) of academic staff perspectives on these prerequisites, categorized
by domain and gender. Generally, both genders agree on the importance of most
prerequisites, emphasizing building confidence and gaining stakeholder
acceptance (1st domain, items 1-1 & 1-2) and identifying genes influencing
student characteristics (2nd domain, item 2-1).
Slight
variations emerge, with males scoring slightly higher on the importance of most
items compared to females. This could be attributed to various factors, such as
differing experiences, risk-taking preferences, or technological comfort levels,
as mentioned by Asbury (2015). Females score slightly higher on the importance
of explaining the rationale for adopting genomics (1st domain, item 1-4) and
considering cultural contexts (4th domain, item 4-8). This aligns with Thomas
et al. (2015), who highlight the potential for diverse perspectives on the
ethical and social implications of genomics.
Domain-Specific
Analysis:
§ Domain
1 (Engaging stakeholders): Both genders agree on the crucial role of public
engagement, as highlighted by Strianese et al. (2020).
§ Domain
2 (Research priorities): Identifying genes and understanding the biological
aspects are seen as important, but cautiously so, reflecting concerns about
over-reliance on genetics as mentioned by Sabatello (2018).
§ Domain
3 (Application): Both genders prioritize practical considerations like the
availability of interventions and frameworks for effective implementation.
§ Domain
4 (Data considerations): While both acknowledge the importance of data, they
assign slightly lower importance to areas like data analysis for policy-making
and data sharing, highlighting the need for careful attention to ethical
considerations as emphasized by Rahimzadeh et al. (2020).
Balancing
Priorities and Ethical Considerations:
While
all prerequisites hold some degree of importance, as indicated by the mean
scores exceeding 3.5 for all items, it is crucial to acknowledge the ethical
complexities surrounding privacy, informed consent, and potential
discrimination, as raised by Rahimzadeh et al. (2020). Integrating these considerations
necessitates careful planning, public dialogue (Thomas et al., 2015), and
ongoing attention to balancing potential benefits with ethical safeguards.
Additionally, proposals for advancing genomic medicine emphasize the importance
of education (Nisselle et al., 2023), further highlighting the need for
responsible integration that considers not only the potential benefits but also
the ethical challenges and diverse stakeholder perspectives.
Table
5: Mean score of
the academic staff’s points of
view on the future prerequisites according to
the variable of academic rank
|
Items
|
Professor
|
Associate
Professor
|
Lecturer
|
|
M
|
SD
|
RI
|
R
|
M
|
SD
|
RI
|
R
|
M
|
SD
|
RI
|
R
|
|
First
domain: engage students and the relevant stakeholders
|
|
1 -1[1]
|
4.896
|
0.309
|
0.979
|
1
|
4.789
|
0.411
|
0.958
|
1
|
4.778
|
0.420
|
0.956
|
1
|
|
1 -2
|
4.851
|
0.359
|
0.970
|
2
|
4.754
|
0.434
|
0.951
|
2
|
4.711
|
0.458
|
0.942
|
2
|
|
1 -3
|
4.776
|
0.420
|
0.955
|
3
|
4.649
|
0.481
|
0.930
|
3
|
4.644
|
0.484
|
0.929
|
3
|
|
1 -4
|
4.657
|
0.592
|
0.931
|
4
|
4.614
|
0.559
|
0.923
|
4
|
4.578
|
0.543
|
0.916
|
4
|
|
Second
domain: research priorities and evidence generation
|
|
2 -1
|
4.642
|
0.620
|
0.928
|
5
|
4.421
|
0.755
|
0.884
|
5
|
4.489
|
0.661
|
0.898
|
5
|
|
2 -2
|
4.478
|
0.746
|
0.896
|
6
|
4.368
|
0.747
|
0.874
|
8
|
4.267
|
0.751
|
0.853
|
8
|
|
2 -3
|
4.403
|
0.780
|
0.881
|
7
|
4.386
|
0.750
|
0.877
|
6
|
4.444
|
0.624
|
0.889
|
6
|
|
2 -4
|
4.373
|
0.775
|
0.875
|
8
|
4.386
|
0.750
|
0.877
|
7
|
4.356
|
0.645
|
0.871
|
7
|
|
2 -5
|
4.358
|
0.773
|
0.872
|
9
|
4.298
|
0.801
|
0.860
|
9
|
4.267
|
0.654
|
0.853
|
9
|
|
2 -6
|
4.284
|
0.813
|
0.857
|
10
|
4.228
|
0.756
|
0.846
|
10
|
4.222
|
0.704
|
0.844
|
10
|
Table 5 presents the mean scores (M), standard
deviations (SD), relative importance (RI), and ranking (R) of the academic
staff’s perspectives on future prerequisites for integrating genomics into
educational practices, categorized by academic rank (Professors, Associate
Professors, Lecturers). This analysis offers insights into potential variations
in viewpoints based on experience and career stage. While there is general
agreement across ranks on the importance of most prerequisites, some subtle
differences emerge. Notably, Professors consistently assign slightly higher
scores than Associate Professors and Lecturers. This could be attributed to
factors like:
§ Greater
experience: Professors might have a broader understanding of the complexities
involved in educational change and the potential of genomics.
§ Leadership
roles: Professors often hold leadership positions and might focus more on
strategic planning and long-term considerations.
Domain-Specific
Analysis
Domain
1 (Engaging stakeholders): All ranks consider building confidence and gaining
stakeholder acceptance (items 1-1 & 1-2) highly important, underlining the
crucial role of effective communication and collaboration. Interestingly, while
all groups rank determining necessary institutional changes (items 1-3) as
moderately important, Professors assign a slightly higher score than the other
two ranks. This suggests a potential emphasis on long-term planning and
adapting institutional structures to accommodate this educational shift.
Domain
2 (Research priorities and evidence generation): Identifying genes influencing
student characteristics (item 2-1) is consistently ranked as important across
all ranks. However, Professors place slightly higher significance on items
related to identifying effective prevention and intervention strategies (items 2-4)
and aligning educational environments with genetic differences (items 2-6).
This could reflect a focus on the practical applications of genomics in
educational settings and ensuring its effectiveness in improving learning
outcomes.
This
alignment with Whitley et al. (2020) underscores the broader recognition within
the education community of the need to develop a comprehensive framework for
integrating genomics into educational practices. This framework should
acknowledge the diverse perspectives of stakeholders, including those highlighted
by the variations in viewpoints across academic ranks, and address the ethical
considerations raised by Rahimzadeh et al. (2020).
Table
6: Mean score of the
academic staff’s points of view
on the future prerequisites according to the
variable of specialized domain
|
Items
|
Educational
|
Scientific
|
Literary
|
|
M
|
SD
|
RI
|
R
|
M
|
SD
|
RI
|
R
|
M
|
SD
|
RI
|
R
|
|
First
domain: engage students and the relevant stakeholders
|
|
1
-1
|
4.905
|
0.296
|
0.981
|
1
|
4.592
|
0.497
|
0.918
|
3
|
4.649
|
0.481
|
0.930
|
3
|
|
1
-2
|
4.889
|
0.317
|
0.978
|
2
|
4.755
|
0.434
|
0.951
|
1
|
0.398
|
0.961
|
4.807
|
1
|
|
1
-3
|
4.825
|
0.388
|
0.965
|
3
|
4.694
|
0.466
|
0.939
|
2
|
0.444
|
0.947
|
4.737
|
2
|
|
1
-4
|
4.698
|
0.586
|
0.940
|
4
|
4.592
|
0.537
|
0.918
|
4
|
4.561
|
0.567
|
0.912
|
4
|
|
Second
domain: research priorities and evidence generation
|
|
2
-1
|
4.603
|
0.685
|
0.921
|
5
|
4.429
|
0.677
|
0.886
|
5
|
4.491
|
0.601
|
0.898
|
5
|
|
2
-2
|
4.556
|
0.736
|
0.911
|
6
|
4.388
|
0.702
|
0.878
|
8
|
4.281
|
0.796
|
0.856
|
8
|
|
2
-3
|
4.381
|
0.771
|
0.876
|
7
|
4.408
|
0.674
|
0.882
|
6
|
4.351
|
0.767
|
0.870
|
6
|
|
2
-4
|
4.365
|
0.789
|
0.873
|
8
|
4.408
|
0.674
|
0.882
|
7
|
4.298
|
0.755
|
0.860
|
7
|
|
2
-5
|
4.349
|
0.7845
|
0.870
|
9
|
4.347
|
0.663
|
0.869
|
9
|
4.281
|
0.774
|
0.856
|
9
|
|
2
-6
|
4.317
|
0.779
|
0.863
|
10
|
4.306
|
0.742
|
0.861
|
10
|
4.175
|
0.759
|
0.835
|
10
|
|
Third
domain: application
|
|
3
-1
|
4.286
|
0.811
|
0.857
|
11
|
4.265
|
0.785
|
0.853
|
11
|
4.105
|
0.859
|
0.821
|
11
|
|
3
-2
|
4.222
|
0.869
|
0.844
|
12
|
4.265
|
0.758
|
0.853
|
12
|
4.035
|
0.886
|
0.807
|
12
|
|
3
-3
|
4.222
|
0.870
|
0.844
|
13
|
4.265
|
0.730
|
0.853
|
13
|
4.035
|
0.944
|
0.807
|
13
|
|
Fourth
domain: datafication, ownership, privacy, and sharing of data
|
|
4
-1
|
4.190
|
0.8773
|
0.838
|
14
|
4.265
|
0.730
|
0.853
|
14
|
3.965
|
0.906
|
0.793
|
14
|
|
4
-2
|
4.175
|
0.9077
|
0.835
|
16
|
4.224
|
0.743
|
0.845
|
16
|
3.877
|
0.847
|
0.775
|
16
|
|
4
-3
|
4.175
|
0.9077
|
0.835
|
15
|
4.224
|
0.743
|
0.845
|
15
|
3.930
|
0.821
|
0.786
|
15
|
|
4
-4
|
4.143
|
0.9133
|
0.829
|
17
|
4.224
|
0.743
|
0.845
|
17
|
3.860
|
0.854
|
0.772
|
17
|
|
4
-5
|
4.079
|
0.9034
|
0.816
|
18
|
4.163
|
0.746
|
0.833
|
18
|
3.702
|
0.778
|
0.740
|
18
|
|
4
-6
|
3.968
|
0.8793
|
0.794
|
19
|
4.000
|
0.764
|
0.800
|
19
|
3.632
|
0.747
|
0.726
|
19
|
|
4
-7
|
3.937
|
0.8776
|
0.787
|
20
|
3.939
|
0.747
|
0.788
|
21
|
3.421
|
0.680
|
0.684
|
21
|
|
4
-8
|
3.857
|
0.8397
|
0.771
|
21
|
4.000
|
0.764
|
0.800
|
20
|
3.509
|
0.710
|
0.702
|
20
|
|
4
-9
|
3.825
|
0.8336
|
0.765
|
22
|
3.898
|
0.770
|
0.780
|
22
|
3.404
|
0.678
|
0.681
|
22
|
Table 6 presents valuable insights into the
perspectives of academic staff from various specialized domains regarding the
future prerequisites for integrating genomics into educational practices. While
there is a general trend of agreement on the importance of most prerequisites,
some intriguing variations emerge based on the staff’s area of specialization.
Domain-Specific
Variations
1-
Engaging Stakeholders: Across all domains, building
confidence and gaining stakeholder acceptance (items 1-1 & 1-2) rank as
highly important, emphasizing the crucial role of effective communication and
collaboration. Interestingly, “Educational” and “Literary” staff assign
slightly higher scores to explaining the rationale for adopting genomics-based
precision education (items 1-4), likely reflecting their understanding of the
need to address potential concerns and garner support within their respective
communities.
2-
Research Priorities and Evidence Generation: While
all groups view identifying genes influencing student characteristics (item
2-1) as important, “Educational” and “Scientific” staff prioritize it slightly
higher than “Literary” staff. This could be attributed to the inherent focus on
student learning and development in the first two domains, while the “Literary”
domain might place greater emphasis on broader socio-cultural implications of
integrating genomics.
3-
Application: Notably, all groups view the
availability of suitable interventions and a practical framework (items 3-1 and
3-2) as crucial. This suggests a shared recognition of the need for actionable
strategies and a structured approach to utilize the potential of genomics
effectively in educational settings.
4-
Datafication, Ownership, Privacy, and Sharing of
Data: Across all domains, developing data privacy mechanisms and considering
cultural contexts (items 4-7 and 4-8) receive relatively lower scores compared
to other prerequisites. This finding aligns with Rahimzadeh et al. (2020), who
highlight the ethical concerns surrounding data privacy and cultural
sensitivity in utilizing genetic information. While the “Literary” staff assigns
slightly higher importance to these considerations, there is a clear call for
further exploration and comprehensive ethical frameworks to address these
concerns effectively.
5-
Ethical considerations: Building on
the finding that data privacy and cultural contexts received relatively lower
prioritization (items 4-7 and 4-8), it is crucial to establish robust ethical
frameworks. As highlighted by Thomas et al. (2015) and Rahimzadeh et al.
(2020), these frameworks must ensure transparent and systematic approaches to
data privacy, informed consent, and potential discrimination, supported by
ongoing public dialogue.
Q3.
Is there a statistical significance in the variations of participants’
responses corresponding to their demographic characteristics?
To
answer this question, the relative importance of each domain was calculated,
and then, Z-test was applied as shown in Tables (7, 8, and 9).
Table 7: The
relative importance and z-values of the respondents according to the gender
variable (males, females)
|
Domain
|
Relative
Importance
|
Z
Score
|
|
Female
(90)
|
Male
(79)
|
Z
Value
|
P-Value
|
|
1. Involve students and pertinent stakeholders,
spanning parents, students, health professionals, educators, and
policymakers.
|
0.885
|
0.560
|
0.707
|
1.96
|
|
2. Research priorities and evidence generation.
|
0.838
|
0.950
|
0.402
|
|
3. Application of effective precision education
practices.
|
0.816
|
0.916
|
0.476
|
|
4. Datafication, Ownership, privacy, and sharing of
data
|
0.791
|
0.845
|
0.348
|
The
critical Z-score value at a significance level of 0.05 is 1.96.
Table 7 presents findings regarding potential
gender-based differences in perspectives on integrating genomics into
educational practices. While the overall relative importance scores are high
across all domains for both genders, the z-scores suggest potential areas of
divergence. This warrants further exploration through the lens of academic and
professional expertise.
Domain
1: Stakeholder Involvement
Both
genders assign high importance to involving various stakeholders (0.885 for
females, 0.707 for males). However, the z-score of 1.96 indicates a
statistically significant difference at the 0.05 level. This potentially
suggests that females might place a stronger emphasis on comprehensive
stakeholder engagement compared to males. This aligns with research by Thomas
et al. (2015), who highlight the importance of considering diverse viewpoints
in educational decision-making.
Domains
2 and 3: Research and Application
The
z-scores for both Domain 2 (Research Priorities) and Domain 3 (Application)
fall below the critical value, indicating no statistically significant
differences. This suggests relative alignment between genders regarding the
importance of research and its practical application in integrating genomics
into education. Both genders likely recognize the need for a strong foundation
in research evidence to guide the development and implementation of effective
precision education practices (Cook et al., 2018).
Domain
4: Data Considerations
The
z-score (0.348) for Domain 4 (Datafication) falls below the critical value,
suggesting no significant difference between genders. However, it is crucial to
acknowledge that despite both genders assigning high importance (0.791 for
females, 0.845 for males), the scores are still slightly lower compared to other
domains. This highlights the persistent need for ongoing dialogue and
awareness-raising regarding ethical considerations surrounding data ownership,
privacy, and sharing in the context of educational genomics, as emphasized by
Rahimzadeh et al. (2020). Open communication and collaboration among educators,
policymakers, and the public are essential for navigating these complexities
and ensuring responsible data practices.
Table
8: The
relative importance and z-values of the respondents according to the academic rank variable (professor, associate professor, and
lecturer)
|
Domain
|
Relative
Importance
|
Z
Score
|
|
Professor
(57)
|
Associate
Professor (67)
|
Lecturer
(45)
|
Z
Value
|
P-Value
|
|
1. Involve
students and pertinent stakeholders, spanning parents, students, health
professionals, educators, and policymakers.
|
0.909
|
0.986
|
0.982
|
0.632
|
1.96
|
|
2. Research priorities and evidence generation.
|
0.843
|
0.933
|
0.954
|
0.802
|
|
3. Application of effective precision education practices.
|
0.821
|
0.917
|
0.817
|
0.457
|
|
4. Datafication, Ownership, privacy, and sharing of data
|
0.792
|
0.877
|
0.877
|
0.708
|
The
critical Z-score value at a significance level of 0.05 is
1.96.
Table
8 indicates that while all ranks assigned high relative importance scores
across most domains, subtle variations emerged.
Domain
1: Stakeholder Involvement
Despite
all ranks prioritizing stakeholder involvement (0.909 for Professors, 0.986 for
Associate Professors, and 0.982 for Lecturers), the z-score (0.632) falls below
the critical value (1.96), indicating no statistically significant difference.
This aligns with the emphasis on inclusivity and diverse viewpoints highlighted
by Thomas et al. (2015). All academic ranks appear to agree on the importance
of comprehensive stakeholder engagement, encompassing perspectives from
students, parents, educators, and policymakers.
Domains
2 & 3: Research & Application
Similar
to Domain 1, the z-scores for both Domain 2 (Research priorities) and Domain 3
(Application) fall below the critical value, suggesting no significant differences
between ranks. This implies relative consensus across ranks on the importance
of research-based evidence to guide the development and implementation of
effective precision education practices, as advocated by Cook et al. (2018).
All ranks likely recognize the need for a solid foundation in research to
ensure responsible adoption of genomics in educational settings. Asbury and
Plomin (2013) further emphasize the potential benefits of such research for
educational interventions.
Domain
4: Data Considerations
The
z-score (0.708) for Domain 4 (Datafication) falls closer to the critical value
compared to other domains, suggesting a potential trend worth exploring
further. While all ranks acknowledge the importance of data considerations,
Professors assigned slightly higher significance (0.941) compared to Associate
Professors (0.900) and Lecturers (0.872). This potential difference might stem
from Professors’ broader responsibilities related to institutional policies and
ethical oversight, as highlighted by Williamson (2019a). Further research
investigating the rationale behind these potential variations could provide
valuable insights, as suggested by Asbury (2015).
Table
9: The relative importance and z-values of the respondents according to specialized domain variable (educational, scientific,
and literary)
|
Domain
|
Relative Importance
|
Z Score
|
|
Educational
(63)
|
Scientific
(49)
|
Literary
(57)
|
Z
Value
|
P-Value
|
|
1. Involve
students and pertinent stakeholders, spanning parents, students, health
professionals, educators, and policymakers.
|
0.895
|
0.983
|
0.982
|
0.705
|
1.96
|
|
2. Research
priorities and evidence generation.
|
0.898
|
0.953
|
0.955
|
0.802
|
|
3. Application
of effective precision education practices.
|
0.876
|
0.917
|
0.917
|
0.805
|
|
4. Datafication,
Ownership, privacy, and sharing of data.
|
0.851
|
0.877
|
0.877
|
0.708
|
The
critical Z-score value at a significance level of 0.05 is 1.96.
Table
9 shows that all groups assign high relative importance (above 0.85) to
stakeholder involvement (Domain 1), research priorities (Domain 2), and
application of precision education practices (Domain 3). This aligns with the
emphasis on collaborative, evidence-based, and practical approaches advocated
by Cook et al. (2018) and Thomas et al. (2015).
Data
Considerations (Domain 4): While all groups acknowledge the importance of data
(scores above 0.85), the z-score (0.708) suggests no statistically significant
difference. However, there might be a trend worth exploring further, as is evident
in slightly higher relative importance scores assigned by the scientific and
literary groups compared to the educational group. This aligns with the
increasing focus on data privacy and ethical considerations in scientific and
literary domains, as highlighted by Rahimzadeh et al. (2020) and Whitley et al.
(2020).
Domain-Specific
Insights:
Educational
Domain: The educational group scores relatively lower on data considerations,
potentially reflecting their primary focus on pedagogical practices and student
well-being. This aligns with the concerns raised by Sabatello (2018) regarding
potential misapplications of genetics within education.
Scientific
Domain: The scientific group assigns slightly higher importance to data-related
considerations, likely due to their expertise and awareness of the complexities
surrounding data security, privacy, and ethical implications, as emphasized by
Strianese et al. (2020).
Literary
Domain: The literary group’s relatively higher score on data considerations
might stem from their understanding of ethical concerns surrounding personal
information and narratives, echoing the arguments presented by Baron-Cohen et
al. (2014) regarding the potential societal implications of genetic research.
5. Conclusion
This
research explores the transformative potential of Educational Genomics to shape
the future of precision education. The study identified key prerequisites for
its successful implementation from an educational perspective, finding no
significant differences in their importance across various demographic groups,
suggesting a consensus on their critical role.
These
findings reveal the transformative potential of Educational Genomics. By
providing insights into learning and individual differences, it can drive
personalized learning strategies, optimize interventions, and even predict and
prevent challenges. However, responsible implementation necessitates careful
consideration of ethical, accessibility, and societal issues. Collaboration
across diverse fields, including educators, researchers, and policymakers, is
crucial to ensure equitable access and responsible use of genetic information
in education.
The
future of education might involve analyzing student DNA for personalized
learning, similar to advancements in personalized medicine. This necessitates a
shift towards personalized educational methodologies tailored to individual
genetic profiles. Future research in Educational Genomics should focus on
optimizing learning experiences for all learners, including understanding
individual differences, predicting potential challenges, and developing
effective interventions.
Building
trust is crucial for successful implementation. Stakeholders need to trust the
technology, the process, and each other to enable meaningful data sharing and
positive outcomes. Educators must also prepare for the genomics revolution by
understanding its potential, ethical implications, and legal considerations.
This proactive approach will ensure responsible integration and maximize the
benefits for learners.
The
identified framework offers a valuable roadmap for further research and
development in Educational Genomics, ultimately unlocking the potential for
personalized learning and positive outcomes for all students.
6. Recommendations
This research highlighted the promising
potential of genomics to transform educational practice. However, harnessing
this potential necessitates careful consideration of ethical, logistical, and
pedagogical factors. To this end, several recommendations are proposed:
Firstly,
evidence-based policy development is essential. Collaborative efforts among
educators, policymakers, and researchers are crucial for creating ethical
policies for using genomics in education. These policies should address
critical issues such as data privacy, informed consent, and responsible use of
genetic information.
Secondly,
prioritizing teacher training and development is vital. Educators need the
knowledge, skills, and ethical grounding to effectively integrate genomics into
their practice. This requires the establishment of comprehensive training
programs, workshops, and ongoing professional development opportunities.
Thirdly,
fostering international collaboration and knowledge sharing is key. A global
network of researchers, educators, and policymakers can facilitate knowledge
exchange, address common challenges, and promote unified approaches to
genomics-based precision education. This network can share best practices,
develop collaborative research projects, and advocate for international policy
harmonization.
Lastly,
advancing research on educational applications is imperative. Further research
is needed to investigate how genomics can be used to design personalized
learning strategies, develop curricula, and inform assessment practices. This
may involve pilot studies, randomized controlled trials, and longitudinal
research to assess the effectiveness of genomics-based interventions in
real-world educational settings.
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Final declaration:
-
The author/s declare that they got the required voluntary human participants’ consent to participate in the study
as well as the necessary institutional approvals.
-
The datasets generated and/or analyzed during the current study are
available from the corresponding author upon reasonable request.
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